Joint Author Sentiment Topic Model

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Information about Joint Author Sentiment Topic Model
Data & Analytics

Published on May 23, 2014

Author: mukherjeesubhabrata

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Joint Author Sentiment Topic Model, Subhabrata Mukherjee, Gaurab Basu and Sachindra Joshi, In Proc. of the SIAM International Conference in Data Mining (SDM 2014), Pennsylvania, USA, Apr 24-26, 2014 [http://people.mpi-inf.mpg.de/~smukherjee/jast.pdf]

Joint Author Sentiment Topic Model Subhabrata Mukherjee Max Planck Institute for Informatics Gaurab Basu and Sachindra Joshi IBM India Research Lab April 25, 2014

April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”        Aspect Rating and Review Rating

April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration      Aspect Rating and Review Rating

April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration  Identify facet sentiments – great (plot), powerful (story), sloppy (acting) etc.     Aspect Rating and Review Rating

April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration  Identify facet sentiments – great (plot), powerful (story), sloppy (acting) etc.  Overall review rating - aggregation of facet-specific sentiments    Aspect Rating and Review Rating

April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration  Identify facet sentiments – great (plot), powerful (story), sloppy (acting) etc.  Overall review rating - aggregation of facet-specific sentiments  Why joint modeling ?  Sentiment words help locating topic words and vice-versa  Neighboring words establish semantics / sentiment of terms Aspect Rating and Review Rating

Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”          April 25, 2014

Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting       April 25, 2014

Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting  Affective sentiment value varies for authors  How much negative is “does not quite make the mark” for me ?     April 25, 2014

Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting  Affective sentiment value varies for authors  How much negative is “does not quite make the mark” for me ?  Author-writing style helps in locating / associating facets and sentiments  E.g. topic switch, verbosity, use of content and function words etc.  The author makes a topic switch in above review using the function word however  April 25, 2014

Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting  Affective sentiment value varies for authors  How much negative is “does not quite make the mark” for me ?  Author-writing style helps in locating / associating facets and sentiments  E.g. topic switch, verbosity, use of content and function words etc.  The author makes a topic switch in above review using the function word however  Traditional works learn a global model independent of the review author April 25, 2014

Why care about writing style or coherence?  Better association of facets to topics by detecting semantic-syntactic class transitions and topic switch  semantic dependencies - association between facets to topics  syntactic dependencies - connection between facets and background words required to make the review coherent and grammatically correct April 25, 2014

Contributions  Show that author identity helps in rating prediction      April 25, 2014

Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences    April 25, 2014

Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences grading style   April 25, 2014

Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences grading style writing style  April 25, 2014

Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences grading style writing style maintain coherence in reviews April 25, 2014

Topic Models April 25, 2014

Topic Models April 25, 2014 1. LDA Model

Topic Models April 25, 2014 1. LDA Model 2. Author-Topic Model

Topic Models April 25, 2014 1. LDA Model 2. Author-Topic Model 3. Joint Sentiment Topic Model

Topic Models April 25, 2014 1. LDA Model 2. Author-Topic Model 3. Joint Sentiment Topic Model 4. Topic Syntax Model

Generative Process for a Review April 25, 2014 Visit Restaurant

Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4

Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topics to write on I will write about food, ambience and …

Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topic Ratings I will give food +5 . It makes awesome Pasta … my favorite !!! But the ambience is loud… I will give it +2. But I do not care about it much Topics to write on I will write about food, ambience and …

Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topic Ratings I will give food +5 . It makes awesome Pasta … my favorite !!! But the ambience is loud… I will give it +2. But I do not care about it much Topics to write on I will write about food, ambience and … Topic Opinion It makes awesome Pasta. But the ambience is loud.

Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topic Ratings I will give food +5 . It makes awesome Pasta … my favorite !!! But the ambience is loud… I will give it +2. But I do not care about it much Topics to write on I will write about food, ambience and … Topic Opinion It makes awesome Pasta. But the ambience is loud. How to write it ?

Generative Process for a Review April 25, 2014 How to write it ?

Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ?

Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ? New Topic ? Current Topic ?

Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ? New Topic ? Current Topic ? Topic Label ?

Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ? New Topic ? Current Topic ? Topic Label ? Word

JAST Model

JAST Model 1. For each document d, author a chooses overall rating r ~ Multinomial(Ω) from author-specific overall document rating distribution

JAST Model 2. For each topic z and each sentiment label l, draw ξz, l ~ Dirichlet(γ) 3. For each class c and each sentiment label l = 0, draw ξc, l ~ Dirichlet(δ)

JAST Model 4. Choose author-specific class transition distribution π Author Writing Style

JAST Model 5. Author a chooses author-rating specific topic-label distribution ϕa, r ~ Dirichlet(α) Author-Topic Preference Author Emotional Attachment to Topics Author Grading Style

JAST Model5. For each word w in the document

JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l).

JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l).

JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l). Semantic Dependencies and Review Coherence

JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l). Review Coherence and Syntactic Dependencies

JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l). d. If c≠ 1, 2, Draw w ~ Multinomial(ξc,l). Review Coherence and Syntactic Dependencies

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Inferencing April 25, 2014

Dataset for Evaluation  IMDB movie review dataset  TripAdvisor restaurant review dataset April 25, 2014

Baselines  Lexical classification using majority voting  Joint Sentiment Topic Model1  Author-Topic LR Model2  Model Prior A sentiment lexicon is used to initialize the prior polarity of words in ξT x L[w] April 25, 2014 1. Chenghua Lin and Yulan He, Joint sentiment/topic model for sentiment analysis, CIKM '09, pp. 375-384. 2. Subhabrata Mukherjee, Gaurab Basu, and Sachindra Joshi, Incorporating author preference in sentiment rating prediction of reviews, WWW 2013.

Model Initialization Parameters April 25, 2014

Model Initialization Parameters April 25, 2014

Model Initialization Parameters April 25, 2014 Minimize Model Perplexity

Model Comparison with Baselines April 25, 2014

Model Comparison with Baselines April 25, 2014 IMDB Movie Review Dataset

Model Comparison with Baselines April 25, 2014 IMDB Movie Review Dataset TripAdvisor Restaurant Review Dataset

April 25, 2014 ComparisonwithTopPerformingModelsinIMDBDataset

April 25, 2014 ComparisonwithTopPerformingModelsinIMDBDataset

April 25, 2014 ComparisonwithTopPerformingModelsinIMDBDataset

Snapshot of Topic-Label-Word Extraction by JAST April 25, 2014

Snapshot of Topic-Label-Word Extraction by JAST April 25, 2014

Snapshot of Topic-Label-Word Extraction by JAST April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014

Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014

Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known      April 25, 2014

Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known  Authorship information helps in identifying author topic preferences, and author writing style to maintain review coherence  Semantic-syntactic class transition and topic switch    April 25, 2014

Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known  Authorship information helps in identifying author topic preferences, and author writing style to maintain review coherence  Semantic-syntactic class transition and topic switch  JAST is unsupervised, with overhead of knowing author identity  Performs better than all unsupervised/semi-supervised models and some supervised models  April 25, 2014

Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known  Authorship information helps in identifying author topic preferences, and author writing style to maintain review coherence  Semantic-syntactic class transition and topic switch  JAST is unsupervised, with overhead of knowing author identity  Performs better than all unsupervised/semi-supervised models and some supervised models  It will be interesting to use JAST for authorship attribution task April 25, 2014

QUESTIONS ??? April 25, 2014

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